About

Covid-19 has accelerated the use of communication technologies. In multilingual settings, this has increased the use of remote interpreting, whereby interpreters are connected to clients via digital platforms. Whilst remote interpreting is challenging, digital platforms create new opportunities to assist interpreters with their tasks. Automatic speech recognition (ASR) has been proposed as a state-of-the-art-technology that might support and revolutionise the practice of interpreters. ASR can provide interpreters with a live transcript of the speech they are listening to and interpreting. One setting in which this could have substantial benefits for interpreters is the legal setting. This is because legal proceedings now often require remote interpreting while communications in court and similar settings can be highly unpredictable, posing challenges for legal interpreters often lacking in training and preparation time. When working remotely, interpreters may face additional problems, such as poor sound quality. However, the research on remote legal interpreting has not yet investigated the use of ASR.

To address this gap in the existing research, this study aims to assess the impact of ASR on interpreters’ performance in remote legal interpreting. The specific objectives are: firstly, to develop a quality assessment model for remote legal interpreting that is sensitive to ASR use; secondly, to explore to what extent ASR can improve the performance of remote legal interpreters; thirdly, to investigate how interpreters adapt to this new technology.

To this end, a two-stage methodology has been designed. Firstly, a comprehensive literature review will be conducted to adapt existing quality assessment approaches to evaluate interpreters’ performance in remote legal interpreting, with and without ASR support. Secondly, empirical data will be collected through multiple resources, including simulated court hearings and reflective sessions with interpreters. Quantitative and qualitative data gathered through these resources will be cross-analysed to draw conclusions regarding the second and third objectives.